This image shows Hannes Mandler

Hannes Mandler


Research associate
Institute of Aerospace Thermodynamics


+49 711 685 62636
+49 711 685 62317

Pfaffenwaldring 31
70569 Stuttgart
Room: 1-125


Heat Transfer  : Development of novel turbulence models based on machine learning methods and application to internal flow systems

  1. Mandler, H., & Weigand, B. (2024). Extrapolation from academic training to industrial test cases: Application of a data-driven closure model to internal cooling channels of gas turbine blades. 1st Workshop on Machine Learning for Fluid Dynamics, Paris, France, 6-8 March 2024.
  2. Mandler, H., & Weigand, B. (2023). Towards interpretable data-driven closure models. 18th European Turbulence Conference, ETC18, Valencia, Spain, 4-6 September 2023.
  3. Mandler, H., & Weigand, B. (2023). Die Entmystifizierung maschinellen Lernens am Beispiel datengetriebener Turbulenzmodellierung. Jahrestreffen Der DECHEMA-Fachgruppen Computational Fluid Dynamics Und Wärme- Und Stoffübertragung, Frankfurt, Germany, 6-8 March 2023.
  4. Mandler, H., & Weigand, B. (2023). Embedding explicit smoothness constraints in data-driven turbulence models. Proceedings of the 14th International ERCOFTAC Symposium on Engineering Turbulence Modelling and Measurements, ETMM14, Castelldefels, Spain, 6-8 September 2023.
  5. Mandler, H., & Weigand, B. (2023). Feature importance in neural networks as a means of interpretation for data-driven turbulence models. Computers & Fluids, 265, 105993.
  6. Mandler, H., & Weigand, B. (2022). A realizable and scale-consistent data-driven non-linear eddy viscosity modeling framework for arbitrary regression algorithms. International Journal of Heat and Fluid Flow, 97, 109018.
  7. Mandler, H., & Weigand, B. (2022). On frozen-RANS approaches in data-driven turbulence modeling: Practical relevance of turbulent scale consistency during closure inference and application. International Journal of Heat and Fluid Flow, 97, 109017.
  8. Gerber, V., Baab, S., Förster, F. J., Mandler, H., Weigand, B., & Lamanna, G. (2021). Fluid injection with supercritical reservoir conditions: Overview on morphology and mixing. The Journal of Supercritical Fluids, 169, 105097.
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